TensorFlow Agents is a library built on top of TensorFlow that provides high-level implementations of common reinforcement learning algorithms. Whether you are a beginner or an experienced RL practitioner, TensorFlow Agents can help you quickly prototype and train RL models.

Reinforcement learning (RL) is a subfield of machine learning that deals with learning through interaction with an environment. RL algorithms learn from trial and error, maximizing a reward signal obtained from the environment. TensorFlow Agents simplifies the implementation of RL models and provides useful tools for training and evaluation.

In this blog post, we will guide you through the process of using TensorFlow Agents in Python. We assume that you have some basic knowledge of TensorFlow and reinforcement learning concepts.

Installation

To get started, you need to install TensorFlow Agents library. You can do this by running the following command:

pip install tf-agents

Make sure you have TensorFlow installed as the library depends on it.

Getting Started

Let’s start by importing the necessary modules:

import tensorflow as tf
from tf_agents.environments import suite_gym
from tf_agents.environments.tf_py_environment import TFPyEnvironment
from tf_agents.policies import random_tf_policy
from tf_agents.agents.dqn import dqn_agent
from tf_agents.replay_buffers import tf_uniform_replay_buffer
from tf_agents.trajectories import trajectory

We will use the OpenAI Gym environment for our example. You can choose any environment from the list available in the suite_gym module.

Next, we will create the environment and wrap it in a TensorFlow environment using the TFPyEnvironment class:

env = suite_gym.load('CartPole-v0')
env = TFPyEnvironment(env)

Now, we need to define the Q-network and the DQN agent:

fc_layer_params = [100]
q_net = q_network.QNetwork(
    env.observation_spec(),
    env.action_spec(),
    fc_layer_params=fc_layer_params)
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=1e-3)
train_step_counter = tf.Variable(0)
agent = dqn_agent.DqnAgent(
    env.time_step_spec(),
    env.action_spec(),
    q_network=q_net,
    optimizer=optimizer,
    td_errors_loss_fn=common.element_wise_squared_loss,
    train_step_counter=train_step_counter)
agent.initialize()

Next, we will create a replay buffer to store the experience:

replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
    data_spec=agent.collect_data_spec,
    batch_size=env.batch_size,
    max_length=100000)

We will also define a random policy to collect initial experience:

policy = random_tf_policy.RandomTFPolicy(
    tf_env.time_step_spec(), 
    tf_env.action_spec())

Now, let’s define a function to collect data using the random policy:

def collect_data(env, policy, buffer, steps):
    for _ in range(steps):
        time_step = env.current_time_step()
        action_step = policy.action(time_step)
        next_time_step = env.step(action_step.action)
        traj = trajectory.from_transition(time_step, action_step, next_time_step)

        # Add trajectory to the replay buffer
        buffer.add_batch(traj)

We can use this function to collect initial experience:

collect_data(env, policy, replay_buffer, steps=1000)

Finally, we will define a function to train the agent:

def train_agent(agent, buffer, steps):
    dataset = buffer.as_dataset(num_parallel_calls=3, sample_batch_size=64, num_steps=2).prefetch(3)
    iterator = iter(dataset)

    for _ in range(steps):
        experience, _ = next(iterator)
        train_loss = agent.train(experience)
        train_step_counter.assign_add(1)

        if train_step_counter.numpy() % 1000 == 0:
            print('Step {}: Loss = {}'.format(train_step_counter.numpy(), train_loss.loss))

You can call the train_agent function to train the agent:

train_agent(agent, replay_buffer, steps=50000)

And that’s it! You have successfully trained a DQN agent using TensorFlow Agents. You can now use the trained model to make predictions and evaluate its performance.

In conclusion, TensorFlow Agents simplifies the implementation and training of reinforcement learning models. It provides high-level abstractions and useful utilities that allow you to focus on solving the specific RL problem at hand. Give it a try and see how it can accelerate your RL development!